Method and system for learned communications signal shaping

ABSTRACT

Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; generating a first RF signal by processing using an encoder machine-learning network; determining a second RF signal that represents the first RF signal altered by transmission through a communication channel; determining a first property of the first signal or the second RF signal; calculating a first measure of distance between a target value of the first property and an actual value of the first or second RF signal; generating second information as a reconstruction of the first information using a decoder machine-learning network; calculating a second measure of distance between the first information and the second information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the first and second measures.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/998,986, filed Aug. 20, 2018, now allowed, which claims priority toU.S. Provisional Application No. 62/547,234, filed on Aug. 18, 2017. Thedisclosure of this prior application is considered part of and isincorporated by reference in the disclosure of this application.

TECHNICAL FIELD

The present disclosure relates to machine learning and deployment ofadaptive wireless communications, and in particular, for learnedcommunications signal shaping for radio frequency (RF) signals.

BACKGROUND

Signal waveforms are prevalent in many systems for communication,storage, sensing, measurements, and monitoring. In some cases, variouswaveforms, such as RF waveforms, acoustic waveforms, or opticalwaveforms, are transmitted and received through various types ofcommunication media, such as over the air, under water, or through outerspace. In some scenarios, RF waveforms transmit information that ismodulated onto one or more carrier waveforms operating at RFfrequencies. In other scenarios, RF waveforms are themselvesinformation, such as outputs of sensors or probes. Information that iscarried in RF waveforms is typically processed, stored, and/ortransported through other forms of communication, such as through aninternal system bus in a computer or through local or wide-areanetworks.

SUMMARY

In general, the subject matter described in this disclosure can beembodied in methods, apparatuses, and systems for training and deployingmachine-learning networks to communicate over RF channels, andspecifically to encode and decode information for learned communicationssignal shaping for communication over RF channels. In some cases, the RFchannels communicate RF signals with specific waveform properties,shapes, or behaviors, or any suitable combination of these.

According to one aspect of the subject matter described in thisapplication, a method is performed by one or more processor configuredto train one or more machine-learning networks to process informationtransmitted through a communication channel. The method includesdetermining first information for transmission through a communicationchannel; generating a first RF signal for transmission through thecommunication channel by processing the first information using anencoder machine-learning network; determining a second RF signal thatrepresents the first RF signal having been altered by transmissionthrough the communication channel; determining a first property of atleast one of the first RF signal or the second RF signal; calculating afirst measure of distance between a target value of the first propertyand an actual value of at least one of the first RF signal or the secondRF signal generating second information as a reconstruction of the firstinformation by processing the second RF signal using a decodermachine-learning network; calculating a second measure of distancebetween the first information and the second information; and updatingat least one of the encoder machine-learning network or the decodermachine-learning network based on (i) the first measure of distancebetween the actual value and the target value of the first property and(ii) the second measure of distance between the first information andthe second information.

The process of updating at least one of the encoder machine-learningnetwork or the decoder machine-learning network includes: determining anobjective function including the first measure of distance and thesecond measure of distance; calculating a rate of change of theobjective function relative to variations in at least one of the encodermachine-learning network or the decoder machine-learning network; anddetermining a goal value of the objective function by using the rate ofchange of the objective function relative to the variations in at leastone of the encoder machine-learning network or the decodermachine-learning network. The goal value corresponds to a value that iswithin a predetermined range from a minimum of the objective function.

Implementations according to this aspect may include one or more of thefollowing features. For example, the process of updating at least one ofthe encoder machine-learning network or the decoder machine-learningnetwork may further include determining at least one of a first weightof the encoder machine-learning network or a second weight of thedecoder machine-learning network, where the first weight or the secondweight enables the objective function to achieve the goal value. In someexamples, the process of determining at least one of the first weight ofthe encoder machine-learning network or the second weight of the decodermachine-learning network includes determining at least one of the firstweight of the encoder machine-learning network or the second weight ofthe decoder machine-learning network to achieve (i) at least one of afirst value that is within a predetermined range from a maximum value ora minimum value of the first measure of distance and (ii) a second valuethat is within a predetermined range from a minimum value of the secondmeasure of distance.

In some implementations, the updating results in a value of the firstmeasure of distance below or above a first predetermined threshold, anda value of the second measure of distance below a second predeterminedthreshold. In some examples, the first property includes a targetspectral shape of at least one of the first RF signal or the second RFsignal, and the process of calculating the first measure of distanceincludes determining a shaping distance metric that represents adifference between the target spectral shape and a spectral shape of atleast one of the first RF signal or the second RF signal. For example,the process of determining the shaping distance metric may includecalculating (i) a summation of squared differences between the spectralshape of the first RF signal and the target spectral shape of the firstRF signal, or (ii) a summation of squared differences between the targetspectral shape of the first RF signal and a maximum among the spectralshape of the first RF signal and a preset threshold value.

In some implementations, the process of determining the first propertyof at least one of the first RF signal or the second RF signal includesusing a known RF processing module to process at least one of the firstRF signal or the second RF signal, and the process of calculating thefirst measure of distance includes determining an output of at least oneof the first RF signal or the second RF signal from the known RFprocessing module as the first measure of distance. For instance, thefirst measure of distance comprises a probability of detection of atleast one of (i) a power level of the first RF signal or the second RFsignal, (ii) a probability of correct classification of the first RFsignal or the second RF signal, (iii) a strength of a moment,correlation, cumulant, or signature of the first RF signal or the secondRF signal, (iv) a frequency of the first RF signal or the second RFsignal, or (v) a cyclostationary property of the first RF signal or thesecond RF signal.

In some implementations, the known RF processing module includes alearned machine-learning network configured to process at least one ofthe first RF signal or the second RF signal, In such implementations,the process of calculating the first measure of distance may includedetermining a plurality of outputs of at least one of the first RFsignal or the second RF signal from the learned machine-learningnetwork, and determining a first output of the plurality of outputs asthe first measure of distance and a second output of the plurality ofoutputs as a third measure of distance that represents a second propertyof the first RF signal, wherein the second property is different fromthe first property of the first RF signal or the second RF signal. Insome examples, the process of determining the objective functionincluding the first measure of distance and the second measure ofdistance includes determining a second objective function including thefirst measure of distance, the second measure of distance, and the thirdmeasure of distance.

In some implementations, the process of updating at least one of theencoder machine-learning network or the decoder machine-learning networkfurther includes determining at least one of a first weight of theencoder machine-learning network or a second weight of the decodermachine-learning network to achieve one or more of (i) a first goalvalue of the first measure of distance, (ii) a second goal value of thesecond measure of distance, and (iii) a third goal value of the thirdmeasure of distance. In such implementations, the first, second, andthird goal values correspond to a value of the second objective functionthat is within a predetermined range from a minimum of the secondobjective function.

In some implementations, the second measure of distance between thesecond information and the first information includes at least one of(i) a cross-entropy between the second information and the firstinformation, (ii) a geometric distance metric between the secondinformation and the first information, or (iii) an f-divergence betweenthe second information and the first information.

In some implementations, the communication channel includes at least oneof a radio communication channel, an acoustic communication channel, anoptical communication channel, or a simulated model channelcorresponding to a radio communication channel, an acousticcommunication channel, or an optical communication channel.

In some implementations, the process of training the at least onemachine-learning network includes one of training, using a transmitterdevice in a communications system that includes the communicationchannel, the encoder machine-learning network, and the decodermachine-learning network, or training, using a receiver device in thecommunications system, the encoder machine-learning network, and thedecoder machine-learning network.

Other implementations of the above and other aspects includecorresponding systems, apparatuses, and computer programs, configured toperform the actions of the above methods, encoded on computer storagedevices. A system of one or more computers can be so configured byvirtue of software, firmware, hardware, or a combination of theminstalled on the system that in operation cause the system to performthe above-described actions. One or more computer programs can be soconfigured by virtue of having instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the above-describedactions.

All or part of the features described throughout this application can beimplemented as a computer program product including instructions thatare stored on one or more non-transitory machine-readable storage media,and that are executable on one or more processing devices. All or partof the features described throughout this application can be implementedas an apparatus, method, or electronic system that can include one ormore processing devices and memory to store executable instructions toimplement the stated functions.

The details of one or more implementations of the subject matter of thisdisclosure are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a radio frequency (RF) system thatupdates machine-learning encoder and decoder networks using two lossfunctions that include a shaping distance metric.

FIG. 2 illustrates an example of a shaping distance metric in afrequency and power domain and example cost functions to determine theshaping distance metric.

FIG. 3 illustrates an example of an RF system that updatesmachine-learning encoder and decoder networks using a loss functionproduced by a known RF processing module and another loss functionrepresenting information loss in reconstructed data.

FIG. 4 illustrates an example of an RF system that updatesmachine-learning encoder and decoder networks using loss functionsproduced by a learned machine-learning RF processing module and anotherloss function representing information loss in reconstructed data.

FIG. 5 is a flowchart illustrating an example method of updatingmachine-learning encoder and decoder networks using multiple distancemetrics.

FIG. 6 is a flowchart illustrating an example method of updatingmachine-learning encoder and decoder networks using an object functionincluding multiple distance metrics.

FIG. 7 is a flowchart illustrating an example method of determining adistance metric between an actual value and a target value of an RFsignal using an output of a known RF processing module.

FIG. 8 is a flowchart illustrating an example method of determining aplurality of distance metrics using a learned machine-learningprocessing module to update encoder and decoder networks based on theplurality of distance metrics.

FIG. 9 is a diagram illustrating an example of a computing system thatmay be used to implement one or more components of a system thatperforms learned communication over RF channels.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Systems and techniques are disclosed herein that enable machine learningand deployment of communication over an impaired RF channel. In someimplementations, at least one machine-learning network is trained toencode information as a signal that is transmitted over a radiotransmission channel, and to decode a received signal to recover theoriginal information. The training may be designed to achieve variouscriteria such a low bit error rate, high power, low power, narrowbandwidth, wide bandwidth, high complexity, low complexity, highdetectability, low detectability, low vulnerability to radio jamming,performing well in particular regimes such as at a low signal to noise(SNR) ratio or under specific types of fading or interference,similarity to pre-existing signal shapes or structure, and/or othercriteria. The results of training such machine-learning networks maythen be utilized to deploy real-world encoders and decoders incommunication scenarios to encode and decode information over varioustypes of RF, acoustic, or optical communication media. In someimplementations, further learning and adaptation of the encoder anddecoder may be implemented during deployment, based on feedbackinformation, for example to further improve performance metrics based onin-situ information and distributions from the deployed location. Theseencoders and decoders may replace or augment one or more signalprocessing functions such as modulation, demodulation, mapping, errorcorrection, or other components which exist in similar communicationssystems today.

In RF communication systems, signal modulation and encoding may resultin a signal shaping of RF signals, or the presence of distinctivesignatures, features or structure. For example, a spectral shape of anRF signal may be different from a spectral shape of a modulated RFsignal when the RF signal is encoded/modulated using an encoding scheme.In some examples, selection processes for signal modulation and encodingschemes are influenced by pre-defined standard modulation schemes,regulatory rules such as spectral power masks, or cost of hardwarecomponents that implement modulation of RF signals. The pre-definedstandard modulation schemes may provide an insufficient modulationperformance considering the various criteria discussed above. In somecases, to minimize interference with an adjacent carrier RF signal, anRF communications system may use pulse shaping filters such as finiteimpulse response filters having a root-raised cosine filter design oranalog filters designed for specific frequency responses. The pulseshaping filters and the analog filters may define a roll-off shape of anRF signal at the edges in the frequency domain.

In some implementations, the selection process for physical layermodulation schemes (e.g., signal encoding schemes) can be learned fromcommunications through channel impairment models using an autoencoder(or an equivalent of the autoencoder) which uses gradient estimationover the communications. For example, the autoencoder may train encoderor decoder machine-learning networks utilizing information included in atransmitted RF signal and a received RF signal that corresponds to thetransmitted RF signal having been altered by transmission through acommunication channel or an analytic model of the communication channel.In one example, the autoencoder seeks a weight or connectivity scheme ofmachine-learning layers included in one or both of the encoder anddecoder networks to minimize a loss of information through thecommunication channel. In this case, the RF communication system maylearn an optimal physical layer modulation scheme from results oftransmission through the communication channel impairment.

In some examples, while learning an optimal physical layer modulationscheme, the RF communication system may determine spectral shapes orother appearances of an RF signal for a wide range of target properties.In the same or other examples, the RF communication system may select aphysical layer modulation scheme from among learned physical layermodulation schemes to achieve a spectral shape of an RF signal for thewide range of target properties. For example, the target propertiesinclude a range of power of a modulated RF signal between certain levels(e.g., from peak to average power), a range of spectrum of the modulatedRF signal, a bandwidth of the modulated RF signal, a target spectralmask, or a frequency shape desired for the modulated RF signal.

In some examples, the target properties may be determined using one moreoutputs of a known or learned signal processing module configured toprocess RF signals. The known processing module may include one or moreknown algorithms that can process RF signals to output actual valuesderived from the RF signals corresponding to the target properties (e.g.detection, classification, localization, symbol estimation, regression,or resilience). By shaping RF signals for capacity of the RF system andfor the target properties, the RF system may enable a learning ofcustomized RF signals that are suited for certain channel environments,improved interoperability, minimized interference, signal band plans,specific application requirements (e.g., military applicationrequirements), or multi-channel operating environments.

In some implementations, a method for learning of radio signal shapingand properties of RF signals is applied in the modulation and encodingprocess of a learned RF encoder network. For example, a target shape ofan RF signal may be achieved by the encoding process of the learnedencoder network rather than by a cascaded process through a separateencoder and a signal shaper such as signal filters. In the same or otherimplementations, a decoder network is trained or optimized to decode theRF signal shaped by the learned encoder network. In these examples, thelearned decoder network may decode various RF signals that havedifferent shapes than an RF signal (i.e., un-shaped modulated RF signal)modulated by idealized or standard modulation schemes. The method forlearning of radio signal shaping and properties of RF signals may beapplied to various applications such as military/government applicationsthat require secure communications, commercial applications that rely ona limited bandwidth capacity, various wireless commination systems, orvarious other communications channels such as acoustic, optical, orthrough other physical mediums.

FIG. 1 illustrates an example of an RF system that updatesmachine-learning encoder and decoder networks using two loss functionsthat include a shaping distance metric. The RF system 100 receives inputdata 108, transmits a representation of the input data 108 through achannel impairment 106, and generates reconstructed data 110corresponding to the input data 108. The RF system 100 includes anencoder network 102 that processes the input data 108 to generate an RFsignal 112 as the representation of the input data 108. The RF system100 further includes a decoder network 104 that extracts informationfrom a received signal 114 that represents the RF signal 112 having beenaltered by transmission through the channel impairment 106. The channelimpairments here in some instance include conversion of a digital signalto and from an analog signal, amplification and mixing of the signal,the effects of noise, fading, distortion, and otherwise on the signal,as well as potential sources of interference or other impairments. Itmay also include residual effects of processing such as synchronizationand/or subcarrier extraction which occurs on the received signal priorto entering one or more decoder network. The decoder network 104generates the reconstructed data 110. The channel impairment 106 may bea physical communication channel such as a radio communication channel,an acoustic communication channel, an optical communication channel, ora simulated model channel corresponding to a radio communicationchannel, an acoustic communication channel, or an optical communicationchannel.

The encoder network 102 and the decoder network 104 may include amachine-learning network or a neural network including a collection ofone or more linear or non-linear algebraic operations. For example, themachine-learning network may include a set of weights andtransformations (e.g. layers of dense matrix weight multiplications,additions, and non-linear rectifications) that can be applied togenerate the RF signal 112 from the input data 108 or to generate thereconstructed data 110 from the received signal 114. A shape (e.g., aspectral shape) of the RF signal 112 may be determined by the weights ortransformations of the machine-learning network included in the encodernetwork 102 (which in some cases may include channel effects such asfixed hardware effects from a radio transmitted which contribute to anaggregate fixed shape upon reception). A decoding accuracy of thedecoder network 104 may be determined by the weights or transformationsof the machine-learning network included in the decoder network 104. Thedecoding accuracy increases as a difference between the input data 108and the reconstructed data 110 decreases. This decoding accuracy couldin some instances reflect the accuracy of decoding correct bits, codewords, symbols, frames, etc.

The RF system 100 includes a network update process 116 that updates theencoder network 102 or the decoder network 104. In the network updateprocess 116, for example, the RF system 100 utilizes a first lossfunction 120 (L1) including a shaping distance metric 118 thatrepresents a difference between an actual shape of the RF signal 112 anda target shape (for example this could be a geometric distance betweenpower spectral densities), and a second loss function 122 (L2) thatrepresents an information loss or alteration between the input data 108and the reconstructed data 110 (for example this could be cross-entropyloss function). In some implementations, the network update process 116updates at least one of the encoder network 102 or the decoder network104 by jointly optimizing the two loss functions L1 and L2. For example,the encoder network 102 and the decoder network 104 may be trained(updated) to minimize or maximize one or both of the two loss functionsL1 and L2.

In one example, goals of the network update process 116 include enablingreliable transport of information through a communications channelincluding the channel impairment 106. For instance, the network updateprocess 116, in optimizing the loss function L2, reduces an error suchas a data bit error between the input data 108 (i.e., transmittedinformation) and the reconstructed data 110 (i.e., recoveredinformation) to a predetermined range. The predetermined range maycorrespond to a tolerable bit error rate that allows reliable transportof information. Such optimization of the loss function L2 may enablereliable wireless propagation of RF signals, for example, between aphone and a communication tower, between two RF transceivers, or betweentwo acoustic transponders.

In some implementations, the goals of the network update process 116include shaping the RF signal 112 to achieve a target value(s) of one ormore signal properties. For instance, the encoder network 102 and thedecoder network 104 may be trained (updated) to minimize the lossfunction L1. In some cases, the encoder network 102 and the decodernetwork 104 may be trained (updated) to bring the loss function L1 to apredetermined range. The loss function L1 may include or be derived froma shaping distance metric 118 that represents a difference a spectralshape of the RF signal 112 and a target spectral shape. In someexamples, the target spectral shape may be determined based on alearning of channel environments, signal band plans, specificapplication requirements, or multi-channel operating environments. Forinstance, the target spectral shape in a military application maycorrespond to a spectral shape that results in a low detectability,reduced interceptability, increased resilience, or other uniquelyfavorable properties. In another, the target spectral shape forcommunications in a noisy environment may correspond to a spectral shapethat results in a high detectability. Further examples of determiningthe shaping distance metric 118 are described below with reference toFIG. 2.

In some implementations, the encoder network 102 and the decoder network104 may be trained (updated) using an objective function including theloss functions L1 and L2. The objective function can be expressed as f(L1, L2) that may include additional terms and operations other than thetwo loss functions L1 and L2. The network update process 116 determinesone or more weights of at least one of the encoder network 102 or thedecoder network 104 based on outputs of the objective function. In someexamples, the network update process 116 may utilize a gradient descenttechnique with back propagation for the joint optimization of the twoloss functions L1 and L2. For example, the network update process 116may use a rate of change of the objective function (e.g., a derivativeof the objective function) to bring a value of the objective function toa goal value that can be a minimum value of the objective function or avalue within a predetermined range from the minimum value. By jointlyoptimizing the two loss functions L1 and L2, the RF system 100 maydetermine an optimum set of weights or connectivity of the encodernetwork 102 or the decoder network 104, which enables a reliablecommunication of information within a threshold data error rate, and asignal shape conforming to a target shape specified by a signal shapingloss function L1.

In some implementations, the encoder network 102 may be trained toachieve various types of objective functions including at least one of ameasure of reconstruction error, a measure of computational complexity,bandwidth, latency, power, or various combinations thereof, and otherobjectives. The decoder network 104 may include a machine-learningnetwork that learns how to decode a received signal 114 intoreconstructed data 110 that approximates the original input data 108.During training, the encoder network 102 and/or decoder network 104 maybe trained by the network update process 116. The encoder network 102and decoder network 104 may be trained to achieve goal values of thevarious types of objective functions.

In scenarios of deployment, the encoder network 102 and decoder network104 may implement encoding and decoding techniques that were previouslylearned from training, or may be (further) trained during deployment.The encoder network 102 and decoder network 104 may be deployed invarious application scenarios to perform communication, using theencoding and decoding representations that were learned during training.In some implementations, the encoder network 102 and/or decoder network104 may be further updated during deployment based on real-timeperformance results such as reconstruction error, power consumption,delay, phase change, frequency shift, etc. In these cases, errorfeedback of loss functions may occur in some instances via acommunications bus, or a protocol message within the wireless systemwhich can be used to update the encoder network 102 and/or the decodernetwork 104, along with information to help characterize the response ofthe RF channel 106.

The input data 108 and reconstructed data 110 may be any suitable formof information that is to be communicated over a channel, such as astream of bits, packets, discrete-time signals, or continuous-timewaveforms (e.g. voice or telemetry). Implementations disclosed hereinare not limited to any particular type of input data 108 andreconstructed data 110, and are generally applicable to learn encodingand decoding techniques for communicating a wide variety of types ofinformation over the RF channel 106 or other forms of communicationschannels.

In some implementations, the encoder network 102 and decoder network 104employ one or more signal processing operations, which are suited to thetype of RF communication domain. As examples, the encoder network 102and/or decoder network 104 may implement filtering, modulation,analog-to-digital (A/D) or digital-to-analog (D/A) conversion,equalization, or other signal processing methods that may be suitablefor a particular types of RF signals or communication domains. Forexample, the network 102 or 104 can implement processes for filtering,modulation, equalization, etc. In some examples, the A/D or D/Aconversion may be performed by a hardware component configured toconvert an output of the encoder network 102 to provide converted outputto a channel that corresponds to the communication channel 106, achannel inside of the encoder network 102, or a channel between thecommunication channel 106 and the decoder network 104. In someimplementations, the encoder network 102 and/or decoder network 104 mayimplement one or more transmit and receive antennas, and other hardwareor software suitable for transmitting signals 112 and receiving signals114 over the RF channel 106.

Therefore, in such scenarios, as shown in the example of FIG. 1, thetransmitted signal 112 and received signal 114 may represent actual RFwaveforms that are transmitted and received over the RF channel 106through one or more antennas. Thus, the encoder network 102 and decodernetwork 104 represent generalized mappings between data 108/110 and RFwaveforms 112/114.

By contrast, in some implementations, the RF system 100 may implementsignal processing and RF transmission/reception processes separatelyfrom the encoder network 102 and decoder network 104. In suchimplementations, one or more signal transmission and/or signal receptioncomponents, such as filtering, modulation, A/D or D/A conversion, singleor multiple antennas, etc., may be represented as part of the channel106. The impairments in the channel 106 may therefore includetransmitter/receiver effects, such as filtering impairments, distortion,interference, additive noise, or other impairments in the transmitterand/or receiver components. Therefore, in such scenarios, thetransmitted signal 112 and received signal 114 represent intermediaterepresentations of data 108/110, and the channel 106 represents ageneral transformation of those intermediate representations ofinformation to and from actual RF waveforms that are transmitted andreceived over an RF medium. For example, the transmitted signal 112 andreceived signal 114 may represent basis coefficients for RF waveforms(e.g., OFDM or wavelet basis function values), time-domain samples of RFwaveforms, distributions over RF waveform values, or other intermediaterepresentations that may be transformed to and from RF waveforms.

In scenarios of training, the reconstructed data 110 may be comparedwith the original input data 108, and the encoder network 102 and/or thedecoder network 104 may be trained (updated) based on results of thereconstruction. In some implementations, updating the encoder network102 and/or decoder network 104 may also be based on other factors, suchas computational complexity of the machine-learning networks (which canbe measured, for example, by the number of parameters, number ofmultiplies/adds, execution time, Kolmogorov complexity, or otherwise),transmission bandwidth or power used to communicate over the channel106, or various combinations thereof and other metrics.

In some implementations, the encoder network 102 and the decoder network104 may include artificial neural networks that include one or moreconnected layers of parametric multiplications, additions, andnon-linearities. In such scenarios, updating the encoder network 102and/or decoder network 104 may include updating weights of the neuralnetwork layers, or updating connectivity in the neural network layers,or other modifications of the neural network architecture, so as tomodify a mapping of inputs to outputs.

The encoder network 102 and the decoder network 104 may be configured toencode and decode using any suitable machine-learning technique. Ingeneral, the encoder network 102 may be configured to learn a mappingfrom input data 108 into a lower-dimensional or higher-dimensionalrepresentation as the transmitted signal 112. Analogously, the decodernetwork 104 may be configured to learn a reverse mapping from alower-dimensional or higher-dimensional received signal 114 into thereconstructed data 110.

As an example, the mappings that are implemented in the encoder network102 and decoder network 104 may involve learning a set of basisfunctions for RF signals. In such scenarios, for a particular set ofbasis functions, the encoder network 102 may transform the input data108 into a set of basis coefficients corresponding to those basisfunctions, and the basis coefficients may then be used to generate atransmitted RF waveform (for example, by taking a weighted combinationof the basis functions weighted by the basis coefficients). Analogously,the decoder network 104 may generate the reconstructed data 110 bygenerating a set of basis coefficients from a received RF waveform (forexample by taking projections of the received RF waveform onto the setof basis functions). The basis functions themselves may be any suitableorthogonal or non-orthogonal set of basis functions, subject toappropriate constraints on energy, amplitude, bandwidth, or otherconditions (e.g., OFDM carriers).

During deployment, in some implementations, the encoder network 102and/or decoder network 104 may utilize simplified encoding and decodingtechniques based on results of training machine-learning networks. Forexample, the encoder network 102 and/or decoder network 104 may utilizeapproximations or compact look up tables based on the learnedencoding/decoding mappings. In such deployment scenarios, the encodernetwork 102 and/or decoder network 104 may implement more simplifiedstructures, rather than a full machine-learning network. Techniques suchas distillation may be used to train smaller networks or otherapproximations which perform the same signal processing function.

In some implementations, the encoder network 102 and/or decoder network104 may include one or more fixed components or algorithms that aredesigned to facilitate communication over RF channels, such as expertsynchronizers, estimators, equalizers, etc. As such, during training,the encoder network 102 and/or decoder network 104 may be trained tolearn encoding/decoding techniques that are suitable for such fixedcomponents or algorithms.

RF signals that are transmitted and received by the RF system 100 mayinclude any suitable radio-frequency signal, such as acoustic signals,optical signals, or other analog waveforms. The spectrum of RF signalsthat are processed by the RF system 100 may be in a range of 1 kHz to300 GHz. For example, such RF signals include very low frequency (VLF)RF signals between 1 kHz to 30 kHz, low frequency (LF) RF signalsbetween 30 kHz to 300 kHz, medium frequency (MF) RF signals between 300kHz to 1 MHz, high frequency (HF) RF signals between 1 MHz to 30 MHz,and higher-frequency RF signals up to 300 GHz.

FIG. 2 illustrates an example of a shaping distance metric in afrequency and power domain and example cost functions to determine theshaping distance metric. For example, an actual signal shape 202 (e.g.,a signal shape corresponding to an RF signal 112 in FIG. 1) is expressedas a function of frequency, S(f), and a target signal shape 204 isexpressed as a function of frequency, T(f). The actual signal shape 202,S(f), may be generated by computation on one or more outputs from theencoder network 102 corresponding to a single or aggregate of many inputdata 108 (e.g., a data bit, a stream of data bits, a symbol, a packet,etc.). The target signal shape 204, T(f), may be a spectral mask thatrepresents a spectral shape or an envelope of signal to comply withrestrictions or standards on radio emitter power envelopes, an optimalsignal shape for mitigating interference with other radio emitters orenvironments, or interoperation with radio transceivers that areexpected to output a specific signal shape for various reasons includingperformance optimization of the radio transceivers.

A shaping distance metric 118 (see FIG. 1) may be determined based on adifference between the actual signal shape 202 and the target signalshape 204. In some examples, the shaping distance metric 118 may includea cost function C₁ that summates or integrates squared errors betweenS(f) and T(f) over a span of frequency. In other examples, the shapingdistance metric 118 may include a cost function C₂ that summates orintegrates over a span of frequency (i) squared errors between S(f) andT(f) in case S(f) is greater than a threshold (zero in this example) and(ii) squared errors between the threshold and the T(f) in case S(f) isless than or equal to the threshold. Many other shaping distance metricsare possible. In the example shown in FIG. 2, the actual signal shape202 and the target signal shape 204 represent power spectral density asa function of center frequency. In other examples, the signal shapes 202and 204 represent other dimensions such as amplitudes, phases, or otherproperties of an RF signal with respect to frequency, time, or distance,etc.

FIG. 3 illustrates an example of an RF system that updatesmachine-learning encoder and decoder networks using a loss functionproduced by a known RF processing module and another loss functionrepresenting information loss in reconstructed data. One or morecomponents of the RF system 300 may correspond to one or more componentsof the RF system 100 of FIG. 1. For instance, the RF system 300 receivesinput data 308, transmits a representation of the input data 308 througha channel impairment 306, and generates reconstructed data 310corresponding to the input data 308. The RF system 300 includes anencoder network 302 that processes the input data 308 to generate an RFsignal 312 as the representation of the input data 308. The RF system300 further includes a decoder network 304 that extracts informationfrom a received RF signal 314 that represents the RF signal 312 havingbeen altered by transmission through the channel impairment 306. Thedecoder network 304 generates the reconstructed data 310. Similar to thechannel impairment 106 in FIG. 1, the channel impairment 306 may be areal-world communication channel or a simulated communication channelrepresenting the real-world communication channel.

The encoder network 302 and the decoder network 304 may include amachine-learning network or a neural network including a collection ofone or more linear algebraic operations. For example, themachine-learning network may include a set of weights andtransformations that can be applied to generate the RF signal 312 fromthe input data 308 or to generate the reconstructed data 310 from thereceived signal 314. One or more signal properties of the RF signal 312may be determined by the weights or transformations of themachine-learning network included in the encoder network 302. A decodingaccuracy of the decoder network 304 may be determined by the weights ortransformations of the machine-learning network included in the decodernetwork 304. For example, the signal properties of the RF signal 312include a shape of the RF signal 312, a probability of detection of atleast one of signal shape, signal power, a correlation strength, signalclassification accuracy, a moment, a frequency, a signature, or acyclostationary property of the RF signal 312, a false alarm rate, afrequency shift, or a SNR of the RF signal 312.

The RF system 300 includes a known RF processing module 324 thatprocesses at least one of the RF signal 312 or the received RF signal314. In some implementations, the RF processing module 324 utilizes oneor more of the RF signal 312 and the received RF signal 314 to determinethe actual values of one or more properties of the RF signal 312. The RFprocessing module 324 may determine one or more signal properties of theRF signal 312 and calculate a distance metric 318 that represents adifference between an actual value of a signal property among the signalproperties and a target value of the signal property. For example, theRF processing module 324 may include a matched filter, an ambiguityfunction, a correlation function, or other energy detection algorithmsthat are available to the RF system 300 to determine actual values ofone or more properties of the RF signal 312.

The RF system 300 may include a network update process 316 that updatesthe encoder network 302 or the decoder network 304. For example, in thenetwork update process 316, the RF system 300 may utilize a lossfunction 320 (L3) corresponding to the distance metric 318 thatrepresents a difference between an actual value of the RF signal 312 anda target value of the signal property, and a second loss function 322(L2) that represents an information loss or alteration from the inputdata 308 to the reconstructed data 310. The network update process 316and the second loss function 322, in terms of updating the encodernetwork 302 or the decoder network 304 based on the second loss function322, may correspond to the network update process 116 and second lossfunction 122, respectively, described above with reference to FIG. 1.

The network update process 316 updates at least one of the encodernetwork 302 or the decoder network 304 by jointly optimizing the twoloss functions L3 and L2. For example, the encoder network 302 and thedecoder network 304 may be trained (updated) to minimize one or both ofthe two loss functions L3 and L2. In some examples, the encoder network302 and the decoder network 304 may be trained (updated) to minimize theloss function L2 and maximize the loss function L3. For instance, in asignal security application, the encoder network 302 may be optimized tominimize a probability of detection, which corresponds to maximizing theloss function L3, while minimizing information loss, which correspondsto minimizing the loss function L2.

In some implementations, the encoder network 302 and the decoder network304 may be trained (updated) using an objective function including theloss functions L3 and L2. The objective function can be expressed as f(L3, L2) that may include additional terms and operations other than thetwo loss functions L3 and L2. The network update process 316 determinesone or more weights of at least one of the encoder network 302 or thedecoder network 304 based on outputs of the objective function. In someexamples, the network update process 316 may utilize a gradient descenttechnique with back propagation for the joint optimization of the twoloss functions L3 and L2. For example, the network update process 316may use a rate of change of the objective function (e.g., a derivativeof the objective function) to bring a value of the objective function toa goal value that can be a minimum value of the objective function or avalue within a predetermined range from the minimum value. By jointlyoptimizing the two loss functions L3 and L2, the RF system 300 maydetermine an optimum set of weights or connectivity of the encodernetwork 302 or the decoder network 304, which enables a reliablecommunication of information within a threshold data error rate, and asignal property conforming to a target value or target shape. In otherexamples, the network update process may adopt an iterative adversarialupdate process wherein optimization cycles between different lossfunctions iteratively or combines them probabilistically (e.g. aWasserstein distance or similar).

FIG. 4 illustrates an example of an RF system that updatesmachine-learning encoder and decoder networks using loss functionsproduced by a learned machine-learning RF processing module and anotherloss function representing information loss in reconstructed data. Oneor more components of the RF system 400 may correspond to one or morecomponents of the RF system 100 of FIG. 1 or the RF system 300 of FIG.3. For instance, the RF system 400 receives input data 408, transmits arepresentation of the input data 408 through a channel impairment 406,and generates reconstructed data 410 corresponding to the input data408. The RF system 400 includes an encoder network 402 that processesthe input data 408 to generate an RF signal 412 as the representation ofthe input data 408. The RF system 400 further includes a decoder network404 that extracts information from a received RF signal 414 whichrepresents the RF signal 412 having been altered by transmission throughthe channel impairment 406. The decoder network 404 generates thereconstructed data 410. Similar to the channel impairment 106 in FIG. 1and the channel impairment 306 of FIG. 3, the channel impairment 406 maybe a real-world communication channel or a simulated communicationchannel representing the real-world communication channel.

The encoder network 402 and the decoder network 404 may include amachine-learning network or a neural network including a collection ofone or more linear algebraic operations. For example, themachine-learning network may include a set of weights andtransformations that can be applied to generate the RF signal 412 fromthe input data 408 or to generate the reconstructed data 410 from thereceived signal 314. One or more signal properties of the RF signal 412may be determined by the weights or transformations of themachine-learning network included in the encoder network 402. A decodingaccuracy of the decoder network 404 may be determined by the weights ortransformations of the machine-learning network included in the decodernetwork 404.

For example, the signal properties of the RF signal 412 include a shapeof the RF signal 412, a probability of detection of at least one ofpower, a moment, a frequency, or a cyclostationary property of the RFsignal 412, a false alarm rate, a frequency shift, or a SNR of the RFsignal 412. In some implementations, the signal properties of the RFsignal 412 correspond to learned properties related to a side effect ora metric of encoding of the RF signal 412. In some examples, two or morelearned properties may be used to optimize the encoder network 402 orthe decoder network 403 by minimizing or maximizing the side effect orthe metric of encoding of the RF signal 412.

The RF system 400 includes a learned RF processing module 424 thatprocesses at least one of the RF signal 412 or the received RF signal414. In some implementations, the learned RF processing module 424utilizes one or more of the RF signal 412 and the received RF signal 414to determine actual values of one or more properties of the RF signal412. The learned RF processing module 424 may determine one or moresignal properties of the RF signal 412 described above to generatedistance metrics 418, each of which represents a difference between anactual value of a signal property among the signal properties and atarget value of the signal property. One example of such a learned RFprocessing module might be a convolutional neural network (CNN) orresidual neural network (ResNet) trained to perform binaryclassification for signal detection, or multi-label classification forsignal classification tasks, taking in raw samples, signalrepresentations, and/or features and producing best learned estimatesfor such signal properties. By training against such a learnedprocessing module adversarially, a communications system can be designedto reduce or improve the efficacy of such learned processing modulesagainst the signal.

In one example, the learned RF processing module 424 may determine asignal property related to a probability of detection and generate afirst distance metric related to an increase of the probability ofdetection and a second distance metric related to a decrease of theprobability of detection. In this case, the second distance metric maybe in an adversarial relation with the first distance metric. In otherexamples, the learned RF processing module 424 determines two or moreindependent signal properties and generate a first distance metricrelated to a first signal property of the independent signal propertiesand a second distance metric related to a second signal property of theindependent signal properties. In some implementations, the distancemetrics 418 include a first representation of a difference between anactual value of the RF signal 412 and a target value of a first signalproperty and a second representation of a difference between an actualvalue of the RF signal 412 and a target value of a second signalproperty.

The RF system 400 includes a network update process 416 that updates theencoder network 402 or the decoder network 404. For example, in thenetwork update process 416, the RF system 400 may utilize (i) a lossfunction 420 (L4) determined from the first representation, (ii) a lossfunction 426 (L5) determined from the second representation, and (iii) aloss function 322 (L2) that represents an information loss or alterationfrom the input data 408 to the reconstructed data 410. As discussedabove, in some cases, the first signal property and the second signalproperty may be dependent on each other, or may correspond to one commonsignal property. For example, the loss function L5 may correspond to anadversarial loss function of the loss function L4 in which the lossfunction L4 seeks to maximize a property of the RF signal 412 while theloss function L5 seeks to minimize the property. In someimplementations, the first signal property and the second signalproperty are independent from each other.

In some implementations, the network update process 416 may determineweights of the encoder network 402 or the decoder network 404 thatminimize or maximize one or both of the two loss functions L4 and L5while minimizing the loss function L2. For example, when the lossfunctions L4 and L5 relate to a SNR and a probability of detection,respectively, the network update process 416 may determine one or moreweights of the encoder network 402 that minimize both loss functions L4and L5. The network update process 416 and the second loss function 422,in terms of updating the encoder network 402 or the decoder network 404based on the loss function 422, may correspond to the network updateprocess 116 and second loss function 122, respectively, described abovewith reference to FIG. 1.

In some implementations, the network update process 416 updates at leastone of the encoder network 402 or the decoder network 404 by jointlyoptimizing the three loss functions L4, L5, and L2. For example, theencoder network 402 and the decoder network 404 may be trained (updated)to minimize one or both of the two loss functions L4 and L5 whileminimizing the loss function L2. In some examples, the encoder network402 and the decoder network 404 may be trained (updated) to minimize theloss function L2, maximize one of the loss functions L4 or L5, andminimize the other of the loss functions L4 or L5. In this case, theloss function L5 may correspond to an adversarial loss function of theloss function L4 as discussed above.

In some implementations, the encoder network 402 and the decoder network404 may be trained (updated) using an objective function including theloss functions L4, L5, and L2. The objective function can be expressedas f (L4, L5, L2) that may include additional terms and operations otherthan the loss functions L4, L5, and L2. The network update process 416determines one or more weights of at least one of the encoder network402 or the decoder network 404 based on outputs of the objectivefunction. In some examples, the network update process 416 may utilize agradient descent technique with back propagation for the jointoptimization of the three loss functions L4, L4, and L2. For example,the network update process 416 may use a rate of change of the objectivefunction (e.g., a derivative of the objective function) to bring a valueof the objective function to a goal value that can be a minimum value ofthe objective function or a value within a predetermined range from theminimum value. By jointly or iteratively optimizing the loss functionsL4, L5, and L2, the RF system 400 may determine an optimum set ofweights or connectivity of the encoder network 402 or the decodernetwork 404, which enables a reliable communication of informationwithin a threshold data error rate, and one or more signal propertiesconforming to their target values or target shapes.

FIG. 5 is a flowchart illustrating an example method 500 of updatingmachine-learning encoder and decoder networks using multiple distancemetrics. The method 500 may be performed by one or more processors, suchas one or more Central Processing Units (CPUs), Graphics ProcessingUnits (GPUs), Digital Signal Processors (DSPs), Field Programmable GateArrays (FPGAs), Application Specific Integrated Circuits (ASICs), TensorProcessing Units (TPUs), or neuromorphic chips, or vector acceleratorsthat execute instructions encoded on a computer storage medium.

In some implementations, the method 500 is used to determine a signalshape of an RF signal using a system 100 that includes a learned machinelearning network such as the encoder network 102, the decoder network104, or both, as described with respect to FIG. 1. In someimplementations, the method 500 is used to update a system 300 thatincludes one or more learned machine learning networks such as theencoder network 302, the decoder network 306, or both, as described withrespect to FIG. 3. In other implementations, the method 500 is used toupdate a system 400 that includes one or more learned machine learningnetworks such as the encoder network 402, the decoder network 406, orboth, as described with respect to FIG. 4. For example, the operationsdescribed in method 500 are performed by one or more processors such asa processor 902 or a processor 952 as described with respect to FIG. 8(below).

The method 500 may be performed by one or more processor configured totrain one or more machine-learning networks to process informationtransmitted through a communication channel. In some examples, themethod 500 includes at least one of: training, using a transmitterdevice in a communications system that includes the communicationchannel, the encoder machine-learning network, and the decodermachine-learning network; or training, using a receiver device in thecommunications system, the encoder machine-learning network, and thedecoder machine-learning network. In some examples, the transmitterdevice and the receiver device may be integrated into a single devicesuch as a radio transceiver.

The method 500 includes determining first information for transmissionthrough a communication channel (502). The first information may be anysuitable discrete-time, analog, discrete-valued, or continuous-valuedinformation or input data. In some instances, this input information maybe whitened discrete bits, packets, or symbols, or in other cases, itmay follow the distribution of a non-whitened information source.

The method 500 further includes generating a first RF signal fortransmission through the communication channel by processing the firstinformation using an encoder machine-learning network (504). Forexample, the first RF signal may represent an analog RF waveform that istransmitted over a channel, or may be an intermediate representation(e.g., samples, basis coefficients, distributions over RF waveforms,etc.) that undergoes further processing (e.g., filtering, D/Aconversion, modulation, etc.) to generate an analog RF waveform. Thisencoding process may utilize any suitable mapping from an inputinformation space into an RF signal space, as discussed in regard toFIG. 1.

The method 500 further includes determining a second RF signal thatrepresents the first RF signal having been altered by transmissionthrough the communication channel (506). For example, in trainingscenarios, the effects of the communication channel may be implementedby a model of a channel obtained by simulation and/or real channel data,or may be implemented by a real-world communication channel. Asdiscussed above, the second RF signal may represent an analog RFwaveform that is received over a channel, or may be an intermediaterepresentation (e.g., samples, basis coefficients, distributions over RFwaveforms etc.) that is a result of processing (e.g., filtering,sampling, equalizing, etc.) a received analog RF waveform.

The method 500 further includes determining a first property of at leastone of the first RF signal or the second RF signal (508). For example,the first property of the first RF signal or the second RF signal may bea signal shape of the first or second RF signal such as an envelope ofpower density over a span of frequency as discussed above with referenceto FIGS. 1 and 2. In some examples, the first property of the first orsecond RF signal represents a probability of energy detection, signalcharacterization or identification, localization, power, bandwidth,complexity of modulation, vulnerability to radio jamming, vulnerabilityto attack, a SNR, a frequency shift, a moment, a cyclostationaryproperty, or a level of fading or interference. In some examples, anetwork update process of an RF system (e.g., RF system 100 in FIG. 1)determines the first property of at least one of the first RF signal orthe second RF signal. In other examples, a separate autoencoder includedin the RF system may determine the first property of at least one of thefirst RF signal or the second RF signal.

The method 500 further includes calculating a first measure of distancebetween a target value of the first property and an actual value of atleast one of the first RF signal or the second signal (510). This firstmeasure of distance may be implemented as a loss function (e.g., theloss function 120 in FIG. 1, the loss function 320 in FIG. 3) or aplurality loss functions (e.g., the loss functions 420 and 426 in FIG.4), and may represent a difference or error between an actual value ofthe first RF signal corresponding to the determined first property and atarget value of the determined first property.

In one example, the first property includes a target spectral shape ofthe first RF signal, and the first measure of distance is a shapingdistance metric that represents a difference between a spectral shape ofthe first RF signal and the target spectral shape of the first RFsignal. For example, the shaping distance metric may be determined bycalculating (i) a summation of squared differences between the spectralshape of the first RF signal and the target spectral shape of the firstRF signal, or (ii) a summation of squared differences between the targetspectral shape of the first RF signal and a maximum among the spectralshape of the first RF signal and a preset threshold value, as discussedabove regarding FIG. 2.

In some implementations, the first measure of distance includes aprobability of detection of at least one of power of the first RF signalor the second RF signal, a probability of correct classification of thefirst RF signal or the second RF signal, a strength of a moment,correlation, cumulant, or signature of the first or second RF signal, afrequency of the first RF signal or the second RF signal, or acyclostationary property of the first RF signal or the second RF signal.

The method 500 further includes generating second information as areconstruction of the first information by processing the second RFsignal using a decoder machine-learning network (512). This decodingprocess may utilize any suitable mapping from an RF signal space intoreconstructed information space, as discussed above in regards to FIG.1.

A second measure of distance is calculated between the secondinformation and the first information (514). The second measure ofdistance may be implemented as a loss function (e.g., the loss function122 in FIG. 1, the loss function 322 in FIG. 3, or the loss function 422in FIG. 4) and may represent a difference or error between the originalinput information and the second (reconstructed) information. Forexample, the second measure of distance may include cross-entropy, amean squared error, a mean average error, or other distance metrics(e.g., f-divergence, Kullback-Leibler divergence, total variationdistance, etc.), or may combine several geometric and/or entropy-baseddistance metrics into an aggregate expression for distance.

The method 500 further includes updating at least one of the encodermachine-learning network or the decoder machine-learning network basedon (i) the first measure of distance between the actual value and thetarget value of the first property and (ii) the second measure ofdistance between the first information and the second information (516).This updating process 516 may be applied to the encoder network and/orthe decoder network in a joint or iterative manner, or individually. Theupdates may generally include updating any suitable machine-learningnetwork feature of the encoder network and/or decoder network, such asnetwork weights, architecture choice, machine-learning model, or otherparameter or connectivity design. As an example, in someimplementations, if the encoder network and/or decoder network aretrained to learn a set of basis functions for communicating over a RFchannel, then the update process 516 may include updating the set ofbasis functions that are utilized in the encoder network and/or decodernetwork.

FIG. 6 is a flowchart illustrating an example method 600 of updatingmachine-learning encoder and decoder networks using an object functionincluding multiple distance metrics. In some implementations, the method600 may be performed to update one or more of the encoder network 102,302, or 402, or the decoder network 104, 304, or 404, as described withrespect to FIGS. 1, 3, and 4. In some examples, the method 600 isperformed to implement the process corresponding to 516 described withrespect to FIG. 5. For example, the method 600 includes determining anobjective function comprising the first measure of distance and thesecond measure of distance (602). For example, as discussed above inFIGS. 1 and 3, the objective function may include a loss function L1/L3determined from the first measure of distance and a loss function L2determined from the second measure of distance. In some cases, each ofthe loss functions L1 and L3 may correspond to the first measure ofdistance. In other cases, the loss functions L1 and L3 may be derivedfrom the first measure of distance. The loss function L2 may correspondto the second measure of distance, or may be derived from the secondmeasure of distance. In some examples, the objective function includesadditional terms and operations other than the loss functions L1, L3,and L2.

The method 600 further includes calculating a rate of change of theobjective function relative to variations in at least one of the encodermachine-learning network or the decoder machine-learning network (604).For example, the rate of change of the objective function may becalculated by a derivative (e.g., δF/δw) of the objective function withrespect to a weight or a parameter of the encoder network or the decodernetwork, where δF indicates a change of the objective function as aconsequence of a change of the weight (δw). In some implementations, therate of change of the objective may be determined in a discrete methodwith discrete changes of the weight.

The method 600 further includes determining a goal value of theobjective function by using the rate of change of the objective functionrelative to the variations in at least one of the encodermachine-learning network or the decoder machine-learning network (606).The goal value may correspond to a value that is within a predeterminedrange from a minimum of the objective function. For example, the goalvalue of the objective function may be achieved when an absolute valueof the rate of change approaches to a certain value (e.g., zero). Insome examples, the method 600 may determine to stop updating the encodernetwork or the decoder network once the objective function reaches avalue within the predetermined range from the minimum of the objectivefunction for an efficient optimization, which may result in a reductionof an optimization time while transporting information within in athreshold error rate.

The method 600 may determine at least one of a first weight of theencoder network or a second weight of the decoder network so that theobjective function can achieve the goal value. For example, the method600 may determine at least one of the first weight of the encodernetwork or the second weight of the decoder network to achieve (i) atleast one of a maximum value or a minimum value of the first measure ofdistance and (ii) a minimum value of the second measure of distance. Theupdating may result in a value of the first measure of distance below orabove a first predetermined threshold, and a value of the second measureof distance below a second predetermined threshold. Alternatively or inaddition, the method 600 may determine one or more weights of theencoder network or the decoder network to minimize the loss function L2in FIGS. 1, 3, and 4, and to maximize or minimize one or more of theloss functions L1, L3, L4, or L5.

FIG. 7 is a flowchart illustrating an example method 700 of determininga distance metric between an actual value and a target value of an RFsignal using an output of a known RF processing module. In someimplementations, the method 700 may be performed to update one or moreof the encoder network 302 or the decoder network 304, as described withrespect to FIG. 3. In some examples, the method 700 is performed toimplement the process corresponding to 508 described with respect toFIG. 5 to determine a first property of at least one of the first RFsignal or the second RF signal by processing at least one of the firstRF signal or the second RF signal through the known RF processing module324. In some examples, the method 700 is performed to implement theprocess corresponding to 510 described with respect to FIG. 5 tocalculate a first measure 318 of distance between an actual value and atarget value of an RF signal using an output 320 of the known RFprocessing module 324. The method 700 includes using a known RFprocessing module to process at least one of the first RF signal or thesecond RF signal (702). For example, as discussed above with referenceto FIG. 3, the known RF processing module may receive an input thatincludes one or both of an RF signal (the first RF signal) generatedfrom an encoder network and a received RF signal (the second RF signal)received through a communication channel to determine a distance metric.

The method 700 further includes determining the first measure ofdistance based on an output of at least one of the first RF signal orthe second RF signal from the known RF processing module (704). Forexample, the known RF processing module may output an actual value or arepresentation of the actual value of a signal property of the first RFsignal. The known RF processing module may determine the first measureof distance that represents a difference between the actual value of thesignal property and a target value of the signal property. As discussedabove with regard to FIG. 3, the RF processing module may include amatched filter, an ambiguity function, a correlation function, or otherenergy detection algorithms, and determine actual values of one or moreproperties of the RF signal. In some implementations, the known RFprocessing module directly outputs the first measure of distance withoutseparately outputting an actual value or a representation of the actualvalue of the signal property.

The method 700 further includes updating at least one of the encodermachine-learning network or the decoder machine-learning network basedon the first measure of distance and the second measure (706). Thisupdating process 706 may correspond to the updating process 516 or themethod 600 described above. For example, at least one of the encodermachine-learning network or the decoder machine-learning network isupdated using the first measure of distance determined from the outputof the known RF processing module and the second measure of distancethat represents a difference between the first information and thesecond information.

FIG. 8 is a flowchart illustrating an example method 800 of determininga plurality of distance metrics using a learned machine-learningprocessing module to update encoder and decoder networks based on theplurality of distance metrics. In some implementations, the known RFprocessing module 324 in FIG. 3 corresponds to or is replaced with alearned RF processing module 424 in FIG. 4. In some implementations, themethod 800 may be performed to update one or more of the encoder network402 or the decoder network 404, as described with respect to FIG. 4. Insome examples, the method 800 is performed to implement the processcorresponding to 508 described with respect to FIG. 5 to determine aplurality of properties of at least one of the first RF signal or thesecond RF signal by processing at least one of the first RF signal orthe second RF signal through the learned RF processing module 424, asdescribed with respect to FIG. 4. In some examples, the method 800 isperformed to implement the process corresponding to 510 described withrespect to FIG. 5 to calculate measures 418 of distance between anactual value and a target value of an RF signal using outputs 420, 426of the learned RF processing module 424. In some examples, the method800 is performed to implement the process corresponding to 516 describedwith respect to FIG. 5 to update at least one of the encoder network 402or the decoder network 404 based on the three loss functions L2, L4, andL5, as described with respect to FIG. 4.

In some examples, the learned RF processing module continues to learn orupdate its weights, parameters, or connectivity that are independentfrom the weights, parameters, or connectivity of the encoder network orthe decoder network. In some examples, the learned RF processing moduleand the encoder/decoder networks are jointly trained using the method800.

The method 800 includes using a learned machine-learning RF processingmodule to process at least one of the first RF signal or the second RFsignal (802). As discussed above with reference to FIG. 4, the learnedRF processing module may receive an input that includes one or both ofan RF signal (the first RF signal) generated from an encoder network anda received RF signal (the second RF signal) through a communicationchannel to determine a plurality of distance metrics (e.g., two, three,or other numbers of metrics). In some examples, the learned RFprocessing module may include a machine-learning network or a neuralnetwork including a collection of one or more linear algebraicoperations to process at least one of the first RF signal or the secondRF signal.

The method 800 further includes determining a plurality of outputs of atleast one of the first RF signal or the second RF signal from thelearned machine-learning network (804). For example, themachine-learning network of the learned RF processing module may includea set of weights and transformations that can be applied to the first RFsignal, the second RF signal, a representation of the first RF signal,or a representation of the second RF signal to generate the plurality ofdistance metrics from the first RF signal or the second RF signal. Insome cases, these plurality of distance metrics may correspond to theplurality of outputs from the learned RF processing module. In somecases, the plurality of distance metrics may be determined by additionaloperations with the plurality of outputs from the learned RF processingmodule.

The method 800 further includes determining a first output of theplurality of outputs as the first measure of distance and a secondoutput of the plurality of outputs as a third measure of distance thatrepresents a second property of the first RF signal or the second RFsignal, where the second property is different from the first property(806). As discussed above, the learned RF processing module outputs aplurality of distance metrics. In some implementations, one of theplurality of distance metrics corresponds to the first measure ofdistance that represents a difference between an actual value of thefirst RF signal and a target value of the first property of at least oneof the first RF signal or the second RF signal. In some implementations,the plurality of distance metrics correspond to a plurality of lossfunctions as discussed with regard to FIG. 4. The first output mayrelate to a first signal property, and the second output may relate to asecond signal property that is different from the first signal property.In some cases, the first output may be correlated or inverselycorrelated to the second output. For instance, a set of weights of theencoder network that maximizes the first measure of distance determinedfrom the first output may minimize the third measure of distancedetermined from the second output. In another example, a set of weightsof the encoder network that minimizes the first measure of distancedetermined from the first output may also minimize the third measure ofdistance determined from the second output.

The method 800 further includes determining an objective functioncomprising the first measure of distance, the second measure ofdistance, and the third measure of distance (808). In someimplementations, a joint optimization of multiple measures of distancemay be realized using an objective function that includes the multiplemeasures of distance. For example, the objective function describedabove with regard to FIG. 4 includes the first measure of distance(e.g., the loss function L4), the second measure of distance (e.g., theloss function L2), and the third measure of distance (e.g., the lossfunction L5). In some cases, each measure of distance may be optimizedindividually or iteratively, which may result in a longer optimizationtime than the joint optimization or an optimization result that is moreor less favorable or biased to a specific measure of distance.

The method 800 further includes updating at least one of the encodermachine-learning network or the decoder machine-learning network basedon the first measure of distance, the second measure, and the thirdmeasure of distance (810). This updating process 810 may expand theupdating process 516 or the method 600 described above. For example, atleast one of the encoder machine-learning network or the decodermachine-learning network are updated using (i) the first measure ofdistance, (ii) the third measure of distance, which are determined fromthe plurality of outputs of the learned RF processing module, and (iii)the second measure of distance that represents a difference between thefirst information and the second information.

In some implementations, the updating process 810 determines at leastone of a first weight of the encoder machine-learning network or asecond weight of the decoder machine-learning network to achieve one ormore of (i) a first goal value of the first measure of distance, (ii) asecond goal value of the second measure of distance, and (iii) a thirdgoal value of the third measure of distance. In this case, the first,second, and third goal values correspond to a value of an objectivefunction that is within a predetermined range from a minimum of theobjective function. For example, when the objective function achievesthe value within the predetermined range from the minimum of theobjective function, the first, second, and third measures of distanceare determined as having been jointly optimized. The updating process810 may update/set the encoder network to include the first weight andthe decoder network to include the second weight.

FIG. 9 is a diagram illustrating an example of a computing system thatmay be used to implement one or more components of a system thatperforms learned communication over RF channels.

The computing system includes computing device 900 and a mobilecomputing device 950 that can be used to implement the techniquesdescribed herein. For example, one or more parts of an encodermachine-learning network system or a decoder machine-learning networksystem could be an example of the system described here, such as acomputer system implemented in any of the machine-learning networks,devices that access information from the machine-learning networks, or aserver that accesses or stores information regarding the encoding anddecoding performed by the machine-learning networks.

In some implementations, the computing device 900 corresponds to a radiotransmitter, receiver, or transceiver that includes at least one of theencoder network 102 or the decoder network 104 in FIG. 1. In this case,the system 100 of FIG. 1 corresponds to the radio transmitter, receiver,or transceiver. In some examples, the system 100 includes one or morecomponents of the computing device 900, for example, such as a processor902 or a memory 904. In some implementations, the mobile device 950corresponds to the system 100 that includes at least one of the encodernetwork 102 or the decoder network 104 in FIG. 1. In some examples, thesystem 100 includes one or more components of the mobile device 950, forexample, such as a processor 952 or a memory 964.

The computing device 900 is intended to represent various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. The mobile computing device 950 is intended torepresent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, cellular base stations,point-to-point or backhaul radios, mobile embedded radio systems,military radios, radio diagnostic computing devices, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 900 includes a processor 902, a memory 904, astorage device 906, a high-speed interface 908 connecting to the memory904 and multiple high-speed expansion ports 910, and a low-speedinterface 912 connecting to a low-speed expansion port 914 and thestorage device 906. Each of the processor 902, the memory 904, thestorage device 906, the high-speed interface 908, the high-speedexpansion ports 910, and the low-speed interface 912, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 902 can process instructionsfor execution within the computing device 900, including instructionsstored in the memory 904 or on the storage device 906 to displaygraphical information for a GUI on an external input/output device, suchas a display 916 coupled to the high-speed interface 908. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Inaddition, multiple computing devices may be connected, with each deviceproviding portions of the operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system). In some implementations,the processor 902 is a single-threaded processor. In someimplementations, the processor 902 is a multi-threaded processor. Insome implementations, the processor 902 is a quantum computer.

The memory 904 stores information within the computing device 900. Insome implementations, the memory 904 is a volatile memory unit or units.In some implementations, the memory 904 is a non-volatile memory unit orunits. The memory 904 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 906 is capable of providing mass storage for thecomputing device 900. In some implementations, the storage device 906may be or include a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 902), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 904, the storage device 906, or memory on theprocessor 902). The high-speed interface 908 manages bandwidth-intensiveoperations for the computing device 900, while the low-speed interface912 manages lower bandwidth-intensive operations. Such allocation offunctions is an example only. In some implementations, the high-speedinterface 908 is coupled to the memory 904, the display 916 (e.g.,through a graphics processor or accelerator), and to the high-speedexpansion ports 910, which may accept various expansion cards (notshown). In the implementation, the low-speed interface 912 is coupled tothe storage device 906 and the low-speed expansion port 914. Thelow-speed expansion port 914, which may include various communicationports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupledto one or more input/output devices, such as a keyboard, a pointingdevice, a scanner, or a networking device such as a switch or router,e.g., through a network adapter.

The computing device 900 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 920, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 922. It may also be implemented as part of a rack server system924. Alternatively, components from the computing device 900 may becombined with other components in a mobile device (not shown), such as amobile computing device 950. Each of such devices may include one ormore of the computing device 900 and the mobile computing device 950,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 950 includes a processor 952, a memory 964,an input/output device such as a display 954, a communication interface966, and a transceiver 968, among other components. The mobile computingdevice 950 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 952, the memory 964, the display 954, the communicationinterface 966, and the transceiver 968, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 952 can execute instructions within the mobile computingdevice 950, including instructions stored in the memory 964. Theprocessor 952 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 952may provide, for example, for coordination of the other components ofthe mobile computing device 950, such as control of user interfaces,applications run by the mobile computing device 950, and wirelesscommunication by the mobile computing device 950.

The processor 952 may communicate with a user through a controlinterface 958 and a display interface 956 coupled to the display 954.The display 954 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface956 may comprise appropriate circuitry for driving the display 954 topresent graphical and other information to a user. The control interface958 may receive commands from a user and convert them for submission tothe processor 952. In addition, an external interface 962 may providecommunication with the processor 952, so as to enable near areacommunication of the mobile computing device 950 with other devices. Theexternal interface 962 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 964 stores information within the mobile computing device950. The memory 964 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 974 may also beprovided and connected to the mobile computing device 950 through anexpansion interface 972, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 974 mayprovide extra storage space for the mobile computing device 950, or mayalso store applications or other information for the mobile computingdevice 950. Specifically, the expansion memory 974 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 974 may be provide as a security module for the mobilecomputing device 950, and may be programmed with instructions thatpermit secure use of the mobile computing device 950. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier suchthat the instructions, when executed by one or more processing devices(for example, processor 952), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 964, the expansion memory 974, ormemory on the processor 952). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 968 or the external interface 962.

The mobile computing device 950 may communicate wirelessly through thecommunication interface 966, which may include digital signal processingcircuitry where useful. The communication interface 966 may provide forcommunications under various modes or protocols, such as GSM voice calls(Global System for Mobile communications), SMS (Short Message Service),EMS (Enhanced Messaging Service), or MIMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), LTE, 5G/6G cellular, among others. Suchcommunication may occur, for example, through the transceiver 968 usinga radio frequency. In addition, short-range communication may occur,such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown).In addition, a GPS (Global Positioning System) receiver module 970 mayprovide additional navigation- and location-related wireless data to themobile computing device 950, which may be used as appropriate byapplications running on the mobile computing device 950.

The mobile computing device 950 may also communicate audibly using anaudio codec 960, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 960 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 950. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 950.

The mobile computing device 950 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 980. It may also be implemented aspart of a smart-phone 982, personal digital assistant, or other similarmobile device.

The term “system” as used in this disclosure may encompass allapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, or multiple processorsor computers. A processing system can include, in addition to hardware,code that creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program (also known as a program, software, softwareapplication, script, executable logic, or code) can be written in anyform of programming language, including compiled or interpretedlanguages, or declarative or procedural languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

Computer readable media suitable for storing computer programinstructions and data include all forms of non-volatile or volatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks ormagnetic tapes; magneto optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry. Sometimes a server is a general-purposecomputer, and sometimes it is a custom-tailored special purposeelectronic device, and sometimes it is a combination of these things.

Implementations can include a back end component, e.g., a data server,or a middleware component, e.g., an application server, or a front endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described is this specification, or anycombination of one or more such back end, middleware, or front endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include a local area network(“LAN”) and a wide area network (“WAN”), e.g., the Internet.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

While this disclosure contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular implementations ofparticular inventions. Certain features that are described in thisdisclosure in the context of separate implementations can also beimplemented in combination in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementationsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a subcombination or variation ofa subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

What is claimed is:
 1. A method performed by one or more processors thatare configured to train one or more machine-learning networks to processinformation transmitted through a communication channel, the methodcomprising: determining first information for transmission through acommunication channel; generating a first RF signal for transmissionthrough the communication channel by processing the first informationusing an encoder machine-learning network; determining a second RFsignal that represents the first RF signal having been altered bytransmission through the communication channel; determining a firstproperty of at least one of the first RF signal or the second RF signal;calculating a first difference measure between a target value of thefirst property and an actual value of at least one of the first RFsignal or the second RF signal; generating second information as areconstruction of the first information by processing the second RFsignal using a decoder machine-learning network; calculating a seconddifference measure between the first information and the secondinformation; determining an objective function using the firstdifference measure and the second difference measure; and updating atleast one of the encoder machine-learning network or the decodermachine-learning network using a value of the objective function.
 2. Themethod of claim 1, wherein updating at least one of the encodermachine-learning network or the decoder machine-learning network furtherusing the value of the objective function comprises: calculating a rateof change of the objective function relative to variations in at leastone of the encoder machine-learning network or the decodermachine-learning network; determining a goal value of the objectivefunction by using the rate of change of the objective function, whereinthe goal value corresponds to a value that is within a predeterminedrange from a minimum value of the objective function; and determining atleast one of a first weight of the encoder machine-learning network or asecond weight of the decoder machine-learning network, wherein the firstweight or the second weight enables the objective function to achievethe goal value.
 3. The method of claim 1, wherein updating at least oneof the encoder machine-learning network or the decoder machine-learningnetwork comprises: determining a first weight of the encodermachine-learning network and a second weight of the decodermachine-learning network to achieve (i) at least one of a first valuethat is within a predetermined range from a maximum value or a minimumvalue of the first difference measure and (ii) a second value that iswithin a predetermined range from a minimum value of the seconddifference measure.
 4. The method of claim 1, wherein the updatingresults in a value of the first difference measure below or above afirst predetermined threshold, and a value of the second differencemeasure below a second predetermined threshold.
 5. The method of claim1, wherein the first property comprises a target spectral shape of atleast one of the first RF signal or the second RF signal, and whereincalculating the first difference measure comprises determining a shapingmetric that represents a difference between the target spectral shapeand a current spectral shape of at least one of the first RF signal orthe second RF signal.
 6. The method of claim 5, wherein determining theshaping metric comprises calculating at least one of (i) a summation ofsquared differences between the current spectral shape of the first RFsignal and the target spectral shape of the first RF signal, or (ii) asummation of squared differences between the target spectral shape ofthe first RF signal and the greater of the current spectral shape of thefirst RF signal and a preset threshold value.
 7. The method of claim 1,wherein determining the first property of at least one of the first RFsignal or the second RF signal comprises using a known RF processingmodule to process at least one of the first RF signal or the second RFsignal, and wherein calculating the first difference measure comprisesdetermining, as the first difference measure, a parameter thatcorresponds to at least one of the first RF signal or the second RFsignal and that is output from the known RF processing module.
 8. Themethod of claim 7, wherein the parameter output from the known RFprocessing module comprises a probability of detection of at least oneof (i) a power level of the first RF signal or the second RF signal,(ii) a probability of correct classification of the first RF signal orthe second RF signal, (iii) a strength of a moment, correlation,cumulant, or signature of the first RF signal or the second RF signal,(iv) a frequency of the first RF signal or the second RF signal, or (v)a cyclostationary property of the first RF signal or the second RFsignal.
 9. The method of claim 7, wherein the known RF processing modulecomprises a learned machine-learning network configured to process atleast one of the first RF signal or the second RF signal, and whereincalculating the first difference measure comprises: determining aplurality of parameters that correspond to at least one of the first RFsignal or the second RF signal and that are output from the learnedmachine-learning network, and determining a first parameter of theplurality of parameters as the first difference measure and a secondparameter of the plurality of parameters as a third difference measurethat represents a second property of at least one of the first RF signalor the second RF signal, wherein the second property is different fromthe first property.
 10. The method of claim 9, wherein determining theobjective function comprises determining a second objective functioncomprising the first difference measure, the second difference measure,and the third difference measure.
 11. The method of claim 10, whereinupdating at least one of the encoder machine-learning network or thedecoder machine-learning network further comprises determining at leastone of a first weight of the encoder machine-learning network or asecond weight of the decoder machine-learning network to achieve one ormore of (i) a first goal value of the first difference measure, (ii) asecond goal value of the second difference measure, and (iii) a thirdgoal value of the third difference measure, and wherein the first,second, and third goal values correspond to a value of the secondobjective function that is within a predetermined range from a minimumvalue of the second objective function.
 12. The method of claim 1,wherein the second difference measure between the second information andthe first information comprises at least one of (i) a cross-entropybetween the second information and the first information, (ii) ageometric distance metric between the second information and the firstinformation, or (iii) an f-divergence between the second information andthe first information.
 13. The method of claim 1, wherein thecommunication channel comprises at least one of a radio communicationchannel, an acoustic communication channel, an optical communicationchannel, a wired communication channel, or a simulated model channelcorresponding to a radio communication channel, an acousticcommunication channel, or an optical communication channel, and whereinthe communication channel is implemented through a plurality of antennascomprising a plurality of communication ports.
 14. The method of claim1, wherein training the one or more machine-learning networks comprisesone of: training, using a transmitter device in a communications systemthat includes the communication channel, the encoder machine-learningnetwork and the decoder machine-learning network, or training, using areceiver device in the communications system, the encodermachine-learning network and the decoder machine-learning network. 15.The method of claim 1, wherein at least one of the encodermachine-learning network or the decoder machine-learning network is apart of a base station or a mobile phone in a cellular network.
 16. Themethod of claim 15, wherein the cellular network includes at least oneof an LTE network, a 5G network, or a 6G network.
 17. The method ofclaim 1, wherein updating at least one of the encoder machine-learningnetwork or the decoder machine-learning network comprises separatelyupdating each of the encoder machine-learning network and the decodermachine-learning network using the objective function.
 18. The method ofclaim 1, wherein determining the second RF signal comprises determiningan RF signal that represents the first RF signal having been altered byinterference with a channel environment during transmission through thecommunication channel.
 19. A non-transitory computer-readable storagemedium having stored thereon instructions which, when executed by atleast one processor, cause performance of operations comprising:determining first information for transmission through a communicationchannel; generating a first RF signal for transmission through thecommunication channel by processing the first information using anencoder machine-learning network; determining a second RF signal thatrepresents the first RF signal having been altered by transmissionthrough the communication channel; determining a first property of atleast one of the first RF signal or the second RF signal; calculating afirst difference measure between a target value of the first propertyand an actual value of at least one of the first RF signal or the secondRF signal; generating second information as a reconstruction of thefirst information by processing the second RF signal using a decodermachine-learning network; calculating a second difference measurebetween the first information and the second information; determining anobjective function using the first difference measure and the seconddifference measure; and updating at least one of the encodermachine-learning network or the decoder machine-learning network using avalue of the objective function.
 20. The storage medium of claim 19,wherein at least one of the encoder machine-learning network or thedecoder machine-learning network is a part of a base station or a mobilephone in a cellular network.
 21. The storage medium of claim 20, whereinthe cellular network includes at least one of an LTE network, a 5Gnetwork, or a 6G network.
 22. A system comprising: at least oneprocessor; and at least one computer memory that is operably connectableto the at least one processor and that has stored thereon instructionswhich, when executed by the at least one processor, cause the at leastone processor to perform operations comprising: determining firstinformation for transmission through a communication channel; generatinga first RF signal for transmission through the communication channel byprocessing the first information using an encoder machine-learningnetwork; determining a second RF signal that represents the first RFsignal having been altered by transmission through the communicationchannel; determining a first property of at least one of the first RFsignal or the second RF signal; calculating a first difference measurebetween a target value of the first property and an actual value of atleast one of the first RF signal or the second RF signal; generatingsecond information as a reconstruction of the first information byprocessing the second RF signal using a decoder machine-learningnetwork; calculating a second difference measure between the firstinformation and the second information; determining an objectivefunction using the first difference measure and the second differencemeasure; and updating at least one of the encoder machine-learningnetwork or the decoder machine-learning network using a value of theobjective function.
 23. The system of claim 22, wherein at least one ofthe encoder machine-learning network or the decoder machine-learningnetwork is a part of a base station or a mobile phone in a cellularnetwork.
 24. The system of claim 23, wherein the cellular networkincludes at least one of an LTE network, a 5G network, or a 6G network.